Primary Cortical Cell Tri-Culture-Based Screening of Neuroinflammatory Response in Toll-like Receptor Activation

The activation of toll-like receptors (TLRs) in the central nervous system (CNS) can lead to neuroinflammation and contribute to many neurological disorders, including autoimmune diseases. Cell culture models are powerful tools for studying specific molecular and cellular mechanisms that contribute to these disease states and identifying potential therapeutics. However, most cell culture models have limitations in capturing biologically relevant phenomena, due in part to the non-inclusion of necessary cell types. Neurons, astrocytes, and microglia (critical cell types that play a role in neuroinflammation) all express at least a subset of TLRs. However, the response of each of these cell types to various TLR activation, along with their relative contribution to neuroinflammatory processes, is far from clear. In this study, we demonstrate the screening capabilities of a primary cortical cell tri-culture of neuron, astrocyte, and microglia from neonatal rats. Specifically, we compare the neuroinflammatory response of tri-cultures to that of primary neuron-astrocyte co-cultures to a suite of known TLR agonists. We demonstrate that microglia are required for observation of neurotoxic neuroinflammatory responses, such as increased cell death and apoptosis, in response to TLR2, 3, 4, and 7/8 activation. Additionally, we show that following TLR3 agonist treatment, microglia and astrocytes play opposing roles in the neuroinflammatory response, and that the observed response is dictated by the degree of TLR3 activation. Overall, we demonstrate that microglia play a significant role in the neuroinflammatory response to TLR activation in vitro and, hence, the tri-culture has the potential to serve as a screening platform that better replicates the in vivo responses.

Introduction
Neuroinflammation plays a significant role in most neurological diseases including multiple sclerosis [1], neurodegenerative diseases [2][3][4], brain injury [5], and stroke [6]. It typically refers to the innate inflammatory response within the central nervous system (CNS), primarily driven by microglia and astrocytes [7]. Following a neuroinflammatory stimulus, microglia and astrocytes become "activated" and can be characterized by changes to their morphology and phenotype [8][9][10]. Depending on a multitude of factors, this response can take on a variety of roles, ranging from rescuing damaged neurons and promoting recovery to increasing pro-inflammatory cytokine expression and increasing cell death [6]. Additionally, it has been shown that cellular cross-talk between neurons and glial cells, some of which require direct cell-cell interaction, plays a significant role in the observed neuroinflammatory response [11,12]. While there has been much progress in determining the cellular and molecular mechanisms driving these disparate outcomes of neuroinflammation, due to the high complexity and interconnectivity, there is still much that is unknown.
Toll-like receptors (TLRs) are an evolutionarily conserved class of pattern-recognition receptors (PRRs) that recognize a multitude of pathogen-associated molecular patterns (PAMPs) produced by bacteria, viruses, and fungi, and are significantly involved in the innate immune response [13]. Additionally, TLRs recognize endogenous damage-associated molecular patterns (DAMPs) such as extracellular matrix components, high-mobility group box protein 1 (HMGB1), and mitochondrial DNA [14]. While TLR expression is primarily associated with macrophages, neutrophils, and other cells associated with the innate immune system, TLRs are expressed in many other cell types, including both neurons and glial cells in the CNS [15,16]. Additionally, CNS-specific factors such as Aβ  , along with DAMPs from damaged or necrotic neuronal cells, have been shown to activate TLRs on both neurons and glial cells, leading to a robust neuroinflammatory response [17,18]. However, the exact nature of the neuroinflammatory response to the activation of different TLRs has been debated. This can in part be attributed in part to the limitations of in vitro methodologies, which are often confined to the culturing of a single cell type. For example, in one study, astrocyte cultures spiked with the TLR3 ligand poly (I:C) showed increased gene expression for many neurotrophic factors, and the conditioned media promoted neuron survival in an organotypic culture [19]. Conversely, a similar study conducted on isolated microglia showed a significant increase in the secretion of pro-inflammatory factors such as TNF-α and IL-6 [20]. Therefore, there is a need to better understand the relative contributions of different CNS cells on the observed neuroinflammatory response to TLR activation.
In this study, we use a newly developed neuron, astrocyte, and microglia tri-culture model that better mimics the in vivo response to a number of neuroinflammatory stimuli [21]. The tri-culture is produced by taking primary cortical cells from neonatal rats and maintaining them in a serum-free culture medium that was specifically designed to support all three cell types. Compared to other mixed-culture models that require the separate maintenance and addition of cells at specific time points, the relative simplicity of this tri-culture model makes it well suited for high-throughput experiments, and an effective model for the early screening of therapeutic molecules. Here, we investigate the response of the tri-culture and neuron-astrocyte co-cultures to a suite of TLR agonists, and quantify the responses of both cultures using a number of known neuroinflammatory markers including changes in cell death, apoptosis, and astrocyte and microglia morphology. We observed significant differences in the neuroinflammatory response between the co-and tri-cultures to nearly all tested TLR agonists, highlighting the role of microglia in neuroinflammatory processes. Additionally, we observed changes in microglia morphology in response to the activation of all tested TLRs (1-9), suggesting that rat microglia express TLR1-9, in agreement with previous studies on mouse [22] and human [20] microglia TLR expression. The results highlight the influence of cell culture composition on the neuroinflammatory response to different TLR agonists, and emphasize that microglia are required in order to observe many of the neurotoxic aspects of neuroinflammation in response to TLR activation found in vivo.
The cells were cultured on tissue culture-treated polystyrene plates (ThermoFisher) or glass bottom chambered coverslips (ibidi, Fitchburg, WI, USA), which were pre-treated by coating the substrates with 0.5 mg/mL of poly-L-lysine (Sigma, Burlington, MA, USA) in B-buffer (3.1 mg/mL boric acid and 4.75 mg/mL borax, Sigma) for 4 h at 37 • C and 5% CO 2 , washing them with sterile deionized (DI) water, and finally covering them with the plating medium.
All procedures involving animals were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, following protocols approved by the University of California, Davis Institutional Animal Care and Use Committee. Timed-pregnant Sprague-Dawley rats were purchased from Charles River Laboratory (Hollister, CA, USA). All animals were housed in clear plastic shoebox cages containing corncob bedding under a consistent temperature (22 ± 2 • C) and 12 h light-dark cycle. Food and water were provided ad libitum. Primary cortical cultures were prepared from perinatal day 0 rat pups as previously described [24]. Neocortices from all pups in the litter were pooled, dissociated, and plated at a density of 550 cells/mm 2 on pre-treated substrates. Primary cortical cells were plated on the plating medium and allowed to adhere for 4 h before the medium was changed to the co-or tri-culture medium. Half-media changes were performed at day in vitro (DIV) 3 and 7 with the respective media types.

TLR Agonist Treatment
The TLR agonists used were as follows (all purchased from InvivoGen, San Diego, CA, USA): Pam3CSK4 (TLR1/2 agonist), heat-killed Listeria monocytogenes (HKLM, TLR2 agonist), Poly(I:C) both high molecular weight (HMW) and low molecular weight (LMW, TLR3 agonist), lipopolysaccharide (LPS, TLR4 agonist), flagellin from Salmonella typhimurium (FLA-ST, TLR5 agonist), FSL1 (TLR6/2 agonist), R848 (Resiquimod, TLR7/8 agonist), and ODN1826 (TLR9 agonist). Prior to use, all ligands were reconstituted in sterile DI water at 200× (the maximum working concentration) and stored at −20 • C. To spike the cultures, at DIV 7, half of the medium was removed from each well and replaced with an equal volume of medium that contained a 2× working concentration of a TLR agonist or an equal volume (~1/200 of the medium volume) of sterile DI water to act as the vehicle control. The cultures were then maintained for 48 h, after which they were analyzed with one of the following methods.

Immunostaining
At the conclusion of the experiment, the cell cultures were washed three times with 37 • C DPBS+ and fixed using 4% w/v paraformaldehyde (PFA; Affymetrix, Santa Clara, CA, USA) in PBS for 2.5 h. Fixed cells were washed twice with 0.05% v/v Tween20 (Sigma) solution in DPBS+, followed by a 3 min permeabilization with 0.1% v/v Triton X-100 (Ther-moFisher) solution in DPBS+ and two additional washes with Tween20 solution. Samples were blocked with a solution of 0.5% v/v heat-inactivated goat serum (ThermoFisher) and 0.3 M glycine (Sigma) in DPBS+ (blocking buffer) for 1 h. Following the blocking step, samples were incubated for 1 h in a primary antibody solution containing mouse anti-βIII tubulin (ThermoFisher), rabbit anti-GFAP (ThermoFisher), and chicken anti-Iba1 (Abcam, Cambridge, UK) in blocking buffer. Samples were then washed three times with Tween20 solution before a 1 h incubation with secondary antibody solution containing goat antimouse antibodies conjugated to AlexaFluor 647 (ThermoFisher), goat anti-rabbit antibodies conjugated to AlexaFluor 488 (ThermoFisher), and goat anti-chicken antibodies conjugated to AlexaFluor 555 (ThermoFisher). Following incubation with secondary antibody solution, the samples were washed three times with DPBS+. Lastly, samples were incubated for 5 min with a 4 ,6-diamidino-2-phenylindole (DAPI) solution (Sigma) to stain cell nuclei, followed by an additional Tween20 solution wash before they were mounted onto glass slides using ProLong Gold Antifade Mountant (ThermoFisher).

Live/Dead, Apoptosis, and Morphological Analysis
Following the 48 h exposure to TLR agonists, each well was incubated with both Hoechst 33342 (ThermoFisher) and propidium iodide (ThermoFisher) directly added to the medium to yield working concentrations of 1 µg/mL and 1 µM, respectively, and incubated for 20 min at room temperature. Apoptotic cells were quantified via annexin V staining using the ab176749 Apoptosis/Necrosis Assay Kit (Abcam) according to the manufacturer's protocol. For morphological analysis, cultures were fixed with a 4% w/v PFA solution in PBS and immunostained as described above. All sample images were acquired with a Zeiss Observer D1 inverted epi-fluorescence microscope at 100× or 200× magnification and analyzed using ImageJ. The number of live/dead cells and percentage of area positive for annexin V staining were quantified using custom ImageJ macros [25]. The average astrocyte/microglia areas were determined by manually tracing the outline of astrocytes/microglia from 200× magnification images and determining the area inside the trace (representative examples are shown in Figure S1).

Statistical Methods
For all experiments requiring image analysis, at least five predetermined fields were analyzed per replicate to account for variability within the culture itself. All data was analyzed using a one-way analysis of variance (ANOVA) test. Differences between groups were identified by a post hoc Tukey test or Dunnett's test. For all experiments, a p-value < 0.05 was considered statistically significant and all data is presented as mean ± standard error of the mean (SEM). All statistical analyses were performed with R.

Influence of Microglia Presence on Cell Death
We have previously demonstrated that there is an improvement in the overall health of neuron, astrocyte, and microglia tri-cultures as compared to neuron-astrocyte co-culture due to the continuous presence of microglia [21]. As expected, we observed a physiologically relevant population of microglia ( Figure 1a) that was not present in the neuronastrocyte co-culture ( Figure 1b). Additionally, we wanted to ensure that the presence of the additional factors added to the tri-culture medium in the absence of microglia did not affect the overall health of the culture. Therefore, we compared the total cell death of the co-culture ( Figure 1d) to both the tri-culture ( Figure 1c) and cultures maintained in the co-culture medium supplemented with TGF-β, cholesterol, or both factors at concentrations used in the tri-culture medium ( Figure 1e). We did not compare conditions with IL-34 as we have previously shown that IL-34 alone is sufficient and necessary for microglia survival in vitro [21], and the present goal was to determine if the additional factors in the tri-culture media (TGF-β and cholesterol) in the absence of microglia had an effect on culture health. As expected, we saw a significant reduction in cell death in the tri-culture (1.63 ± 0.32% dead cells) as compared to the co-culture (22.33 ± 0.44% dead cells, p < 1.0 × 10 −7 ). Additionally, there was no change in cell death when TGF-β (23.06 ± 0.86% dead cells), cholesterol (20.89 ± 1.10% dead cells), or both factors (24.46 ± 0.67% dead cells) were added to the co-culture medium, which all showed a significant increase in cell death as compared to the tri-culture (p < 1.0 × 10 −7 for all conditions vs. tri-culture).

Figure 1.
Representative fluorescence images of the (a) tri-culture and (b) co-culture at DIV 9 demonstrating the presence of microglia in the tri-culture. The cultures were immunostained for the three cell types of interest: neurons-anti-βIII-tubulin (red), astrocytes-anti-GFAP (green), microglia-anti-Iba1 (orange), general nuclear stain DAPI (blue). As expected, microglia are only present in the tri-culture (highlighted with arrows). Representative images of (c) tri-cultures and (d) cocultures stained with Hoechst 33342 (Live, blue) and propidium iodide (Dead, red). (e) Comparison of the influence of TGF-β and increased cholesterol concentration on cell death in the absence of microglia as compared to the co-culture and tri-culture (containing all three additional factors). Data presented as mean ± SEM (n = 4-8 wells from two independent dissections) with individual data points plotted, *** p < 0.001 (as determined by post hoc Tukey's test). Scale bar = 100 µm.

Influence of TLR Agonists on Cell Death
In order to determine the role of microglia on the overall neuroinflammatory impact of TLR activation, we compared the response of both the co-and tri-cultures to an array of known TLR agonists at a range of concentrations ( Figure 2). These concentrations were individually chosen for each TLR agonist based on concentrations that have been shown to elicit a response from immune cells and covered two orders of magnitude [21,[26][27][28][29][30][31]. As expected, we observed a significant increase in cell death in the tri-culture following a 48 h incubation with LPS (TLR4 agonist) at all concentrations (p < 2 × 10 −16 , p = 1.1 × 10 −5 , and p = 6.6 × 10 −4 when incubated with 5 µg/mL LPS, 500 ng/mL and 50 ng/mL, respectively) as compared to the vehicle, which was not observed in the co-culture (p = 1.0 for all concentrations). We also observed a significant increase in cell death following treatment with the highest concentration of HKLM (1 × 10 8 cells/mL, TLR2 agonist) in the tri-culture (p = 9.04 × 10 −4 ), and while treatment with 1 × 10 8 cells/mL HKLM did show an increase in cell death in the co-culture, this increase did not prove to be statistically significant (p = 0.10). Interestingly, we saw a non-monotonic relationship between HMW poly (I:C) (TLR3) treatment concentration and cell death, where treatment with 1 µg/mL HMW poly (I:C) induced a significant increase in cell death (p = 5.45 × 10 −9 ), while the 10 µg/mL and 100 ng/mL treatment concentrations did not result in cell death (p = 0.21 & 1.0, respectively). For the co-culture, treatment with 10 µg/mL HMW poly (I:C) led to a significant reduction in cell death (p = 0.023), which was the only instance of a reduction of cell death across all conditions. Additionally, for the co-culture there were trends towards increased cell death following treatment with 100 ng/mL Pam3SK4 (TLR1/2 agonist, p = 0.066), 1 × 10 8 cells/mL HKLM (TLR2 agonist, p = 0.10), and 100 ng/mL FSL1 (TLR6/2 agonist, p = 0.14). Figure 1. Representative fluorescence images of the (a) tri-culture and (b) co-culture at DIV 9 demonstrating the presence of microglia in the tri-culture. The cultures were immunostained for the three cell types of interest: neurons-anti-βIII-tubulin (red), astrocytes-anti-GFAP (green), microglia-anti-Iba1 (orange), general nuclear stain DAPI (blue). As expected, microglia are only present in the tri-culture (highlighted with arrows). Representative images of (c) tri-cultures and (d) cocultures stained with Hoechst 33342 (Live, blue) and propidium iodide (Dead, red). (e) Comparison of the influence of TGF-β and increased cholesterol concentration on cell death in the absence of microglia as compared to the co-culture and tri-culture (containing all three additional factors). Data presented as mean ± SEM (n = 4-8 wells from two independent dissections) with individual data points plotted, *** p < 0.001 (as determined by post hoc Tukey's test). Scale bar = 100 µm.

Influence of TLR Agonists on Cell Death
In order to determine the role of microglia on the overall neuroinflammatory impact of TLR activation, we compared the response of both the co-and tri-cultures to an array of known TLR agonists at a range of concentrations ( Figure 2). These concentrations were individually chosen for each TLR agonist based on concentrations that have been shown to elicit a response from immune cells and covered two orders of magnitude [21,[26][27][28][29][30][31]. As expected, we observed a significant increase in cell death in the tri-culture following a 48 h incubation with LPS (TLR4 agonist) at all concentrations (p < 2 × 10 −16 , p = 1.1 × 10 −5 , and p = 6.6 × 10 −4 when incubated with 5 µg/mL LPS, 500 ng/mL and 50 ng/mL, respectively) as compared to the vehicle, which was not observed in the co-culture (p = 1.0 for all concentrations). We also observed a significant increase in cell death following treatment with the highest concentration of HKLM (1 × 10 8 cells/mL, TLR2 agonist) in the triculture (p = 9.04 × 10 −4 ), and while treatment with 1 × 10 8 cells/mL HKLM did show an increase in cell death in the co-culture, this increase did not prove to be statistically significant (p = 0.10). Interestingly, we saw a non-monotonic relationship between HMW poly (I:C) (TLR3) treatment concentration and cell death, where treatment with 1 µg/mL HMW poly (I:C) induced a significant increase in cell death (p = 5.45 × 10 −9 ), while the 10 µg/mL and 100 ng/mL treatment concentrations did not result in cell death (p = 0.21 & 1.0, respectively). For the co-culture, treatment with 10 µg/mL HMW poly (I:C) led to a significant reduction in cell death (p = 0.023), which was the only instance of a reduction of cell death across all conditions. Additionally, for the co-culture there were trends towards increased cell death following treatment with 100 ng/mL Pam3SK4 (TLR1/2 agonist, p = 0.066), 1 × 10 8 cells/mL HKLM (TLR2 agonist, p = 0.10), and 100 ng/mL FSL1 (TLR6/2 agonist, p = 0.14).

Figure 2.
Influence of TLR agonist treatment on cell death as quantified by staining the cultures with Hoechst 33342 (Live) and propidium iodide (Dead). The overall percentage of live cells in (a) cocultures and (b) tri-cultures following TLR agonist treatment. Data shown as mean ± SEM (n = 6 wells from two independent dissections) with individual data points plotted, * p < 0.05, *** p < 0.001 (as determined by a one-way ANOVA followed by Dunnett's test vs. vehicle control).

Influence of TLR Agonists on Apoptosis
Motivated by the influence of TLR agonist treatment on cell death in both the co-and tri-culture configurations, we studied whether TLR agonist treatment induced other, potentially subtle, changes in the cultures. Therefore, we compared the amount of Annexin V staining (a marker for apoptosis) in both the co-and tri-cultures in response to TLR agonist treatments. Specifically, we treated the cultures with the highest concentration of each TLR agonists along with 1 µ g/mL HMW poly (I:C) as we observed a difference in cell death in the tri-culture at this concentration that was not observed at the higher concentration. We observed significantly more Annexin V staining in the control conditions of the co-culture (12.27 ± 0.72%) as compared to the tri-culture (7.05 ± 0.51%, p = 5.61 × 10 −5 ) (Figure 3a,b) in agreement with our previous study which used caspase 3/7 activity to quantify apoptosis [21]. Similar to cell death, we observed a significant decrease in the Annexin V staining in the co-culture treated with the 10 µ g/mL HMW poly (I:C) (TLR3 agonist) as compared to the control (p = 3.55 × 10 −4 , Figure 3c) that was not observed in the tri-culture (p = 0.99, Figure 3d). For the tri-culture we observed a significant increase in Annexin V staining following treatment with 10 µ g/mL R848 (TLR7/8 agonist, p = 0.036, Figure 3d). Additionally, we saw a trend towards increased Annexin V staining following treatment with 5 µ g/mL LPS (TLR4 agonist, p = 0.094) and 1 µ g/mL HMW poly (I:C) (p = 0.17). Overall, we observed that compared to the co-culture, the tri-culture showed a more robust an increase in AnnexinV staining in response to TLR agonist treatment ( Figure S2).

Influence of TLR Agonists on Apoptosis
Motivated by the influence of TLR agonist treatment on cell death in both the coand tri-culture configurations, we studied whether TLR agonist treatment induced other, potentially subtle, changes in the cultures. Therefore, we compared the amount of Annexin V staining (a marker for apoptosis) in both the co-and tri-cultures in response to TLR agonist treatments. Specifically, we treated the cultures with the highest concentration of each TLR agonists along with 1 µg/mL HMW poly (I:C) as we observed a difference in cell death in the tri-culture at this concentration that was not observed at the higher concentration. We observed significantly more Annexin V staining in the control conditions of the coculture (12.27 ± 0.72%) as compared to the tri-culture (7.05 ± 0.51%, p = 5.61 × 10 −5 ) (Figure 3a,b) in agreement with our previous study which used caspase 3/7 activity to quantify apoptosis [21]. Similar to cell death, we observed a significant decrease in the Annexin V staining in the co-culture treated with the 10 µg/mL HMW poly (I:C) (TLR3 agonist) as compared to the control (p = 3.55 × 10 −4 , Figure 3c) that was not observed in the tri-culture (p = 0.99, Figure 3d). For the tri-culture we observed a significant increase in Annexin V staining following treatment with 10 µg/mL R848 (TLR7/8 agonist, p = 0.036, Figure 3d). Additionally, we saw a trend towards increased Annexin V staining following treatment with 5 µg/mL LPS (TLR4 agonist, p = 0.094) and 1 µg/mL HMW poly (I:C) (p = 0.17). Overall, we observed that compared to the co-culture, the tri-culture showed a more robust an increase in AnnexinV staining in response to TLR agonist treatment ( Figure S2).

Figure 3.
Influence of TLR agonist treatment on Annexin V staining. Representative fluorescence images of the (a) co-culture and (b) tri-culture at DIV 9. Quantification of the Annexin V staining in the (c) co-cultures and (d) tri-cultures following 48 h exposure to TLR agonists. Data presented as mean ± SEM (n = 4 wells from two independent dissections) with individual data points plotted, * p < 0.05, *** p < 0.001 (as determined by a one-way ANOVA followed by Dunnett's test vs. vehicle control). Scale bar = 100 µ m.

Influence of TLR Agonists on Glial Cell Morphology
Reactive astrocytes, typically characterized by a hypertrophic morphology, are often used as markers of neuroinflammation in neurodegenerative [32] and autoimmune disorders [33]. Therefore, we quantified the change in astrocyte area in both the co-and tricultures in response to TLR agonist treatment (Figure 4a-c). Upon exposure to TLR agonists, we did not observe any significant changes to astrocyte area in the co-cultures (Figure 4d). However, for the tri-culture, significant astrocyte hypertrophy was evident following treatment with 1 µ g/mL PAM3CSK4 (p = 0.033) and 5 µ g/mL LPS (p = 0.044) ( Figure  4e). Change to microglia morphology is another common marker used to identify neuroinflammation, and we have previously shown that in culture, microglia adopt a spread morphology in response to neuroinflammatory stimuli [21]. Therefore, we also quantified the change in microglia morphology in the tri-culture following TLR agonist treatment. We observed a significant increase in microglia area for every treatment condition with the exception of 10 µ g/mL HMW poly (I:C) and 10 µg/mL LMW poly (I:C) (Figure 4f).

Influence of TLR Agonists on Glial Cell Morphology
Reactive astrocytes, typically characterized by a hypertrophic morphology, are often used as markers of neuroinflammation in neurodegenerative [32] and autoimmune disorders [33]. Therefore, we quantified the change in astrocyte area in both the co-and tri-cultures in response to TLR agonist treatment (Figure 4a-c). Upon exposure to TLR agonists, we did not observe any significant changes to astrocyte area in the co-cultures (Figure 4d). However, for the tri-culture, significant astrocyte hypertrophy was evident following treatment with 1 µg/mL PAM3CSK4 (p = 0.033) and 5 µg/mL LPS (p = 0.044) (Figure 4e). Change to microglia morphology is another common marker used to identify neuroinflammation, and we have previously shown that in culture, microglia adopt a spread morphology in response to neuroinflammatory stimuli [21]. Therefore, we also quantified the change in microglia morphology in the tri-culture following TLR agonist treatment. We observed a significant increase in microglia area for every treatment condition with the exception of 10 µg/mL HMW poly (I:C) and 10 µg/mL LMW poly (I:C) (Figure 4f). microglia area in the tri-cultures following 48 h exposure to TLR agonists. Data presented as mean ± SEM (n = 4 wells from two independent dissections) with individual data points plotted, * p < 0.05, ** p < 0.01, *** p < 0.001 (as determined by a one-way ANOVA followed by Dunnett's test vs. vehicle control). Scale bar = 100 µ m.

Discussion
In vitro methods are powerful tools for studying the specific cellular and molecular mechanisms behind many neurological and neurodegenerative diseases [34,35]. However, they are also limited in capturing the in vivo microenvironment that is composed of complex physical and biochemical cues. One way to improve in vivo relevance of an in vitro model (e.g., cell culture) is to incorporate critical cell types to maintain cell-to-cell communication, such as including neurons and glial cells in the context of cortical neural tissue, to better mimic inflammation and other pathological states [36]. To that end, we studied the influence of the neural cell culture's composition on its neuroinflammatory response to an array of TLR agonists. We specifically revealed the important role of microglia by using rodent primary cortical cell cultures with and without microglia. Additionally, we demonstrated the significant benefit of the tri-culture model's relative simplicity for use in screening large numbers of treatments.
In our previous work, we demonstrated that the continuous presence of microglia in a tri-culture of neurons, astrocytes, and microglia did not negatively affect the overall health of the culture and resulted in decreased caspase 3/7 activity (a marker for apoptosis) [21]. Here, we further show that the presence of viable and functional microglia sig- microglia area in the tri-cultures following 48 h exposure to TLR agonists. Data presented as mean ± SEM (n = 4 wells from two independent dissections) with individual data points plotted, * p < 0.05, ** p < 0.01, *** p < 0.001 (as determined by a one-way ANOVA followed by Dunnett's test vs. vehicle control). Scale bar = 100 µm.

Discussion
In vitro methods are powerful tools for studying the specific cellular and molecular mechanisms behind many neurological and neurodegenerative diseases [34,35]. However, they are also limited in capturing the in vivo microenvironment that is composed of complex physical and biochemical cues. One way to improve in vivo relevance of an in vitro model (e.g., cell culture) is to incorporate critical cell types to maintain cell-to-cell communication, such as including neurons and glial cells in the context of cortical neural tissue, to better mimic inflammation and other pathological states [36]. To that end, we studied the influence of the neural cell culture's composition on its neuroinflammatory response to an array of TLR agonists. We specifically revealed the important role of microglia by using rodent primary cortical cell cultures with and without microglia. Additionally, we demonstrated the significant benefit of the tri-culture model's relative simplicity for use in screening large numbers of treatments.
In our previous work, we demonstrated that the continuous presence of microglia in a tri-culture of neurons, astrocytes, and microglia did not negatively affect the overall health of the culture and resulted in decreased caspase 3/7 activity (a marker for apoptosis) [21]. Here, we further show that the presence of viable and functional microglia significantly increases the percentage of live cells in the culture as compared to a sole neuron-astrocyte co-culture, and that this increase is mediated by the presence of microglia (dictated by the presence of IL-34 [37][38][39]) and not by the additional factors (i.e., TGF-β or cholesterol alone) present in the tri-culture medium required to maintain physiologically active microglia (Figure 1). This confirms that the improved culture health is primarily due to cross-talk between microglia, neurons, and astrocytes, and changes the cytokine profile of the culture as discussed in our previous publication [21].
Previous work has shown that microglia from mice [22] and humans [20] constitutively express mRNA for TLR1-9, and our results suggest that rat microglia also constitutively express TLR1-9. Specifically, following 48 h exposure to TLR agonists, we observed a significant increase in microglia area, indicative of microglia activation [40][41][42][43] for all TLR agonists with the exception of LMW poly(I:C) (Figure 4f). However, both LMW poly(I:C) and HMW poly(I:C) activate TLR3, indicating that microglia respond to TLR3 agonists, but only at specific concentrations, as it has been shown that LMW poly(I:C) has a much lower activation efficiency for TLR3 as compared to HMW poly(I:C) [44]. Additionally, while we observed morphological changes in response to TLR agonist treatments, not all treatments induced further indicators of neuroinflammation such as changes to cell death or apoptosis. Conversely, while it has been suggested that astrocytes express mRNA for TLR1-9 [15,45,46], it has been shown that astrocytes express mRNA for TLR3 at much higher levels than the other TLRs [20]. Similarly, when quantifying the different aspects of neuroinflammation in the co-culture lacking microglia, we only observed changes to cell death and apoptosis in response to TLR3 activation, with no other TLR ligands inducing changes to cell death, apoptosis, or astrocyte morphology. Therefore, while we have not directly quantified the expression levels of different TLRs in the astrocytes and microglia in our cultures, the results from this study suggest that conclusions drawn from mouse and human studies can be extended to our primary rat tri-culture model.
Of all the TLR agonists tested, FLA (TLR5 agonist), FSL1 (TLR6/2 agonist), and ODN1826 (TLR9 agonist) did not show any additional indicators of neuroinflammation in either the co-or tri-cultures beyond changes to microglia morphology. Compared to other TLRs, the role of TLR5 activation in relationship to neuroinflammation and other neurological diseases has received less attention, with much of the research focusing on the expression levels of TLR5 in different CNS cell types and not on the role TLR5 activation may have on neuroinflammation [17,46]. One recent study has shown that activation of TLR5 on microglia induces neural cell loss and apoptosis using a microglia and neuron co-culture [47]. Interestingly, we did not observe the same neurotoxic effects although we used the same source and concentration of TLR5 agonists for our treatment. One likely cause of this discrepancy may be the difference in the culture methodology, including animal model (rat vs. mouse), age (perinatal vs. embryonic), and culture composition (neuron-microglia co-culture vs. tri-culture). Another potential reason for the increased neurotoxic features observed by Ifuku et al. is that the isolated microglia culture used in their study contained 10% fetal bovine serum (FBS), which has been shown to activate microglia [48], and may be working in combination with the TLR5 activation to produce a more pronounced neurotoxic effect. Activation of TLR9 on microglia has also been shown to induce significant neuronal death in vivo [49] that was not observed in our tri-culture. However, this cell death was primarily attributed to the breakdown of the blood brain barrier (BBB) [49], and therefore it is not surprising that the tri-culture was unable to capture this effect.
Activation of TLR4 in microglia by LPS is a well characterized method to induce robust neurotoxic neuroinflammation both in vitro [50] and in vivo [51]. The results from this study are in line with our previous work showing significant increases in apoptosis and astrocyte and microglia hypertrophy in the tri-culture following exposure to LPS [21]. While we do not observe a statistically significant increase in AnnexinV staining in this study, the relative change of AnnexinV staining vs. control is similar in magnitude to the increase in caspase 3/7 activity in our previous study, and thus the lack of statistical significance in this study (p = 0.094) may be a function of the need to control for the familywise error rate (in the case of multiple comparisons). In addition, we demonstrate a significant increase in cell death in the tri-culture following 48 h exposure to LPS at concentrations as low as 50 ng/mL confirming that the tri-culture can replicate the robust neuroinflammatory response to LPS treatment seen in vivo. Similarly, the response of the co-culture is as expected with no changes to cell death, AnnexinV staining, or astrocyte morphology, as the culture lacks microglia.
TLR2 has a wide spectrum capable of recognizing a variety of PAMPs [52], and TLR1 and 6 heterodimerize with TLR2 to further enhance the recognition of different PAMPs. The heterodimerization has been attributed to an evolutionary mechanism of expanding the ligand recognition spectrum of various cell types to the diverse lipopeptide structure of different pathogens [53]. In this study, TLR2 was activated with HKLM, which is a well-known agonist that is used both for in vivo [54] and in vitro [55,56] studies. Along with TLR2, the TLR1, and TLR6 heterodimers were activated with PAM3CSK4 and FSL-1 additions to the culture, respectively. We observed an increasing trend of cell death in the co-culture with TLR1/2, 2 and 6/2 agonists, whereas the cell death in the corresponding tri-culture groups was negligible except for in the tri-culture group treated with the highest concentration of the TLR2 agonist. Surprisingly, while cell death was not significant for the tri-culture group treated with TLR1/2 agonist, there was significant astrocyte hypertrophy for this group only (in contrast to the groups treated with TLR 2 and 6/2 agonists). The underlying reasons for these seemingly contradictory phenomena require mechanistic studies and may be due to the dual effects (anti-inflammatory vs. pro-inflammatory) of some cytokines secreted as function of TLR activation (e.g., IL-6) [57] and the cooperative response of these TLRs exhibiting non-monotonous dependence on agonist concentrations. Importantly, these results highlight that the presence of microglia reveals complex responses compared to those from neuron-astrocyte co-cultures.
The opposite responses of the co-and tri-cultures to exposure to TLR3 agonist highlights the importance of culture composition on the observed outcomes of neuroinflammatory stimuli. The neuron-astrocyte co-culture shows a significant decrease in cell death and AnnexinV staining in following 48 h exposure to 10 µg/mL HMW poly(I:C). This is in line with previous reports showing that isolated astrocyte cultures produce neuroprotective cytokines in response to TLR3 activation, and that conditioned media from poly(I:C)-treated purified astrocyte cultures improved neuron survival in organotypic cultures [19]. Conversely, it has been shown that activation of microglial TLR3 leads to increased production of the pro-inflammatory cytokines TNF-α and IL-6 [58]. Interestingly, the tri-culture was able to capture both the neuroprotective aspect of TLR3 activation on astrocytes and the more neurotoxic aspect of microglia TLR3 activation leading to a non-monotonic change in cell death in response to HMW poly(I:C) concentration in the tri-culture. We observed a significant increase in cell death following treatment with 1 µg/mL HMW poly(I:C), which was not observed at higher (10 µg/mL) treatment concentrations, putatively due to the neuroprotective aspects of astrocyte TLR3 activation counteracting the neurotoxic aspects of microglia TLR3 activation.
We did not observe a significant change in cell death or astrocyte morphology in either the co-or tri-culture following exposure to R848 (TLR7/8 agonist). However, in the tri-culture we did see a significant increase in AnnexinV staining that was not seen in the co-culture. While there has not been much research into the impact of astrocyte TLR7/8 activation [17,46], it has been shown that activation of TLR8 in primary microglia cultures from non-human primates leads to an increase in production of pro-inflammatory cytokines such as TNF-α and IL-12 [59]. Additionally, knocking-down (KD) of TLR7 in mice lead to a reduction in the production of IL-6 following infections with Japanese encephalitis virus (JEV); however, these mice also showed increased viral load and mortality [60]. It has also been shown that R848 treatment can directly interact with TLR8 receptors on neurons, leading to reduced neurite outgrowth and increased apoptosis [61]. As we did not observe an increase in apoptosis in the co-culture, it is unlikely that direct activation of TLR8 on the neurons was solely responsible for the increase in AnnexinV staining in the tri-culture.
It is most likely that the increase in proinflammatory factors driven by the activation of microglia TLR7/8, or a combination of the two effects, is responsible for the increased apoptosis observed in the tri-culture.

Conclusions
In this study, we demonstrated the importance of microglia on the observed neuroinflammatory response to TLR activation in primary cortical cultures (summarized in Table 1). In response to TLR2, 3, 4, and 7/8, we observed significant neurotoxic responses including increases in cell death and apoptosis in tri-cultures of neurons, astrocytes, and microglia that were not observed in co-cultures of only neurons and astrocytes. The only significant change that we observed in the co-culture was a decrease in cell death and apoptosis in response to TLR3 activation. Future studies comparing changes in cytokine production would further the understanding of the role microglia play in the neuroinflammatory response to TLR activation. Additionally, comparing the co-activation of different TLRs may also reveal differences in the neuroinflammatory response, as previous work has shown unique neuroinflammatory responses to different pairwise combinations of TLR agonists [62]. Cell culture models are vital tools for gaining a better understanding of the cellular and molecular processes underlying many neurological and neurodegenerative disorders. However, it is also critical to recognize the inherent limitations within these models. The use of mixed culture models, such as the tri-culture used in this study, is one method of overcoming some of these limitations. By including neurons, astrocytes, and microglia in a single culture model, we were able to observe the relative influence of all three cell types on the neuroinflammatory response to different TLR agonists. This was particularly apparent in the response to TLR3 activation, in which we observed that microglia and astrocytes play opposing roles in the neuroinflammatory response and depending on the concentration of TLR3 agonist the response of microglia or astrocytes dominated the observed response. Additionally, as the only modification required to establish and maintain the tri-culture is the preparation and use of the specific tri-culture medium, we demonstrate that the tri-culture is well suited for higher-throughput experiments. Overall, we have demonstrated that microglia play a significant role in the neuroinflammatory response to TLR activation in vitro, and their presence is critical to better replicate the in vivo response. Furthermore, we expect that the tri-culture model and the accompanying methods described here will be useful for higher-throughput screening studies that range from neurotoxins to pharmaceuticals. Table 1. Summary of co-and tri-culture response to TLR activation as compared to their respective vehicle controls.

Toll-like Receptor Activation
Co-Culture Tri-Culture  Figure S1: Representative fluorescence images of glia morphology of all TLR agonist treatments; Figure S2: Comparison of the change in the number of live cells, AnnexinV staining, and astrocyte area between the co-and tri-cultures following TLR agonist treatment.
Author Contributions: N.G. and E.S. designed the experiments. N.G. performed the cell culture, imaging, live/dead and apoptosis assays, and general data analysis. H.K. conducted the imaging and morphological analysis. N.G. and H.K. wrote the manuscript. E.S. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Institutes of Health via NINDS/NIA R03-NS118156, NIBIB R21-EB024635, and NCCIH R21-AT010933. This project benefited from the resources of the MIND Institute IDDRC Core services (NICHD P50-HD103526).
Institutional Review Board Statement: All procedures involving animals were conducted in accordance with the Nation-al Institutes of Health Guide for the Care and Use of Laboratory Animals, following protocols approved by the University of California, Davis Institutional Animal Care and Use Committee.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.

Conflicts of Interest:
The authors declare no conflict of interest.


s2fieldsofstudy: Biology Medicine

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Cross-talk between HIF and PD-1/PD-L1 pathways in carcinogenesis and therapy

Tumor-associated hypoxia plays an important role in carcinogenesis and metastasis. The expression, activation, and stabilization of hypoxia-inducible transcription factors (HIFs) support malignant cell survival, proliferation, plasticity, and motility. Hypoxia also upregulates the expression of programmed cell death ligand 1 (PD-L1) in malignant and immune regulatory cells. Therefore, the combination of HIF inhibitors and checkpoint inhibitors (CPIs) is promising for boosting antitumor immunity and diminishing malignant cell plasticity and therapy resistance. In this issue of the JCI, Salman et al. report the development of a specific agent that inhibited HIF-1/2–mediated gene expression in tumor cells and suppressed tumor growth. Bailey, Liu, et al. in this issue demonstrate that targeting HIF-1α abrogated PD-L1–mediated immune evasion by suppressing PD-L1 expression on malignant and myeloid regulatory cells, causing tumor rejection. These findings suggest that targeting the HIF/PD-L1 axis with specific HIF inhibitors should improve the safety and efficacy of CPIs for cancer therapy.

Hypoxia, which is also known as oxygen deficiency and is commonly seen in the solid tumor environment because of an erratic tumor vasculature and high metabolic rate, plays an important regulatory role in cancer development, tumor growth and spreading, formation of distant metastases, and cancer dormancy. The essential molecular issue in hypoxia is the activation and stabilization of hypoxia-inducible transcription factors (HIFs) (1). HIF-1, the main transcription factor activated in hypoxic conditions, is composed of two subunits, α and β, which have similar conformations. Under hypoxic conditions, when HIF-1α is stable, it translocates to the cell nucleus and heterodimerizes with HIF-1β. In the nucleus, HIF-1 initiates the transcription of numerous genes regulating cellular energy metabolism, function, and survival, and are collectively known as hypoxia-responsive genes (1). There are also two homologs that associate with HIF-1α; HIF-2α shares some functionality and targeted genes with HIF-1α, and HIF-3α may negatively regulate HIF-1α and HIF-2α functions (2).
HIF-1 promotes tumor progression via the stimulation of angiogenesis, immunosuppression, epithelial-mesenchymal transition (EMT), and metabolic reprogramming, supporting malignant cell survival, proliferation, plasticity, and motility (ref. 1 and Figure  1). Transient acute hypoxic conditions and chronic hypoxia generate a metabolically diverse tumor microenvironment (3), which induces an intense conditional pressure on cancerous cells that encourages the survival of aggressive malignant clones. Hence, in a clinical setting, hypoxia correlates with worse prognosis in different cancer types and, in addition, imparts resistance to different therapeutic approaches (4).
Numerous experimental and clinical data reveal that hypoxia induces radioresistance and resistance to a variety of chemotherapeutic agents, affects antitumoral immune responses, and controls crucial regulatory pathways, impacting key aspects of cancer biology. For instance, hypoxia impacts the efficiency of cancer immunotherapy by contributing to the immune-eliminated phenotype, which is a key barrier to the therapeutic effect of adoptive cell therapy (5). Therefore, hypoxia signaling in cancer is an apparent target for intervention and the suppression of hypoxia-associated signaling may be considered a valuable treatment option in diseases with limited therapeutic choices. Clinical studies of HIF inhibitors indicate benefit and justify further evaluation either as a single agent or in combination with other antitumor agents (6).

Targeting HIFs
Various signaling pathways control the expression, accumulation, and activity of HIF-1 protein throughout its signaling cycle inside cells, suggesting that targeting HIF-1 might be an encouraging approach for cancer drug development. In fact, several approaches that target hypoxic malignant cells are presently in preclinical and clinical evaluation. Many small molecules that target HIF-1α at the mRNA or protein level block HIF-1α and HIF-1β heterodimerization, promote HIF-1 degradation, or interfere with DNA-binding activity in the nucleus to suppress HIF-1 activity have been introduced in recent years (7). For instance, two small molecule HIF-2α inhibitors, MD6482 (also known as PT2977) and PT2385, exhibited promising activities in experimental models, and based on the results of clinical testing in participants with advanced renal cell carcinoma, MK6482 became the first FDA-approved and the HIF-1 inhibitors would counteract the capacity of malignant cells to adapt to the subsequent conditions and develop therapy resistance (Figure 1). This suggestion is based on data showing that a hypoxic environment permits escape of cancerous cells from killing by cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells via granzyme B degradation and TGF-βcontaining extracellular vesicles (15,16). In addition, hypoxia upregulates expression of immune checkpoint molecules, such as VISTA and programmed cell death ligand 1 (PD-L1), on myeloid-derived suppressor cells (MDSCs), promoting their immunosuppressive phenotype (17,18).

PD-1/PD-L1 axis in cancer
Programmed cell death 1 (PD-1) receptor is expressed on different immune cells, including tumor-infiltrating lymphocytes (TILs), and binds to PD-L1, which is expressed on myeloid regulatory cells and nonimmune cells such as malignant cells (Figure 1). Inducible expression of PD-L1 at the tumor site is involved in cancer escape because its binding to PD-1 on effector T cells causes T cell exhaustion and apoptosis, while apoptosis of regulatory T cells is inhibited (19). PD-L1 expression has been correlated with poor clinical outcomes, and PD-1/PD-L1targeted inhibitors can lead to effective clinical responses in some patients with different types of cancer. Combination therapies based on PD-1/PD-L1 blockade for a wide range of cancer types have been introduced and can improve patient survival (20). One approach uses HIF inhibitors in combination with anti-PD-1/PD-L1 therapy.

PD-L1/HIF cross-talk and therapeutic applicability
Importantly, HIF-1α inhibition synergizes with anti-PD-1 therapy to inhibit tumor development (Figure 1). For instance, in vivo treatment with PX-478 and anti-PD-1 antibodies suppresses tumor growth and prolongs animal survival, which is associated with downregulation of EMT phenotypes, lower immunosuppression, and enhanced TIL homing (21). In a murine melanoma model, inhibition of HIF-1α transcriptional activity results in CCL2-and CCL5-mediated increases in NK cells and CTLs in the tumor bed, and improves the antitumor potential of peptide vaccination and anti-PD-1 blocking antibody (22). target signal transduction pathways. For instance, the DNA alkylating agent temozolomide demonstrated greater antitumor efficacy if applied together with HIF-1 inhibition in a glioma model (13). Alternatively, some approved drugs known to indirectly alter the HIF-1α pathway could also act as adjuvant therapy for existing cancer treatments (6). For example, mTOR inhibitors, known to translationally attenuate HIF-1α protein expression, synergize with rapamycin in reducing tumor growth in preclinical models of hepatocellular carcinoma (14).
Other data show that HIF-1 inhibitors may contribute to the antiangiogenic and antitumor effects of different agents that

Figure 1. Cross-talk between HIF and PD-1/PD-L1 pathways in carcinogenesis and therapy.
A majority of solid tumors develop hypoxia because of chaotic vascularization leading to deprivation of optimal oxygen supply and increased cellular proliferation and metabolic rate. Hypoxia causes the activation and stabilization of key transcription factors, the hypoxia-inducible transcription factors (HIFs). HIF-1α and HIF-2α control the expression of many tumorigenesis genes. HIFs promote tumor progression by stimulating angiogenesis, immunosuppression, EMT, and metabolic reprogramming, and by supporting malignant cell survival, motility, proliferation, plasticity, and enhancing treatment resistance and escape from a nutrient-deprived milieu. Hypoxia also upregulates the expression of PD-L1 in malignant cells and MDSCs via HIF-1α and HIF-2α. PD-L1, via binding to its receptor (PD-1), acts as a protumorigenic factor that induces immune tolerance within the tumor microenvironment and inhibits antitumor immune responses by suppressing activity of tumor-specific TILs. HIF inhibition synergizes with CPIs to block tumor development and progression. Further studies should determine how other factors in the tumor milieu, such as cytokines, chemokines, growth factors, hormones, and therapeutic agents, may interfere with the antitumor potential of a combination of HIF inhibitors and CPI therapy. different CPIs. Promotion of immune tolerance by differential regulation of PD-L1 in normal and cancer tissues is an important approach for safer and more successful immunotherapy (24).

Conclusions
PD-L1 expression is controlled by several signaling pathways. Recent data indicate that hypoxia is also able to upregulate expression of PD-L1 in malignant cells via HIF-1α and HIF-2α (17). For instance, analysis of the melanoma genome atlas data shows a correlation of the HIF-1 signaling signature with PD-L1 mRNA expression, especially with IFN-γ-induced expression of PD-L1 mRNA (25). Bailey, Liu, et al. confirmed these results (24). The findings on differential regulation of PD-L1 expression by HIF suggest that a combination of HIF inhibition with some CPIs, like anti-CTLA-4, for example, should be experimentally verified. Salman et al. also support the notion that highly specific and nontoxic HIF inhibitors deserve further investigation when applied alone or in combination with CPIs and other targeted therapy (23). Testing these approaches in different tumor models will be crucial to developing effective cancer treatments. Understanding the potential synergistic effects of HIF-1 on cells in the cancer milieu with other tumor-associated factors like TGF-β, IL-1, hormones, and growth factors is another important direction for multitargeted approaches and for development of safe biological therapies. Studies must also reveal strategies that elaborate the effect of HIF inhibitors on cancer stem cell progenitors, intratumoral endothelial cells, and cancer-associated fibroblasts in conjunction with immune effectors and regulators to control the tumor metastatic potential and chemo-and radioresistance in different models.
In this issue of the JCI, Salman et al. report on the development of a low-molecular-weight agent that inhibited HIF-1/2mediated gene expression in tumor cells and suppressed tumor growth (23). The synthesized low-molecular-weight agent targeted both HIF-1 and HIF-2, inducing HIF-α subunit degradation and thus inhibiting activation of HIF-1-and HIF-2-target gene transcription. Genes affected by this inhibition encoded regulators of angiogenesis, glycolysis, and immunity. Administration of the HIF inhibitor in tumor-bearing mice resulted in inhibition of tumor growth. The smaller tumors were associated with lower tumor vascularization due to decreased expression of multiple genes encoding angiogenic factors, and lower immunosuppression due to diminished expression of genes encoding proteins mediating immunosuppression. Key metabolic and signal-transduction pathways driving cancer progression were also blocked. In vivo alterations of the tumor immune environment involved fewer protumorigenic tumor-associated macrophages (TAMs) and MDSCs due to the suppression of IL-4, IL-13, and CXCL1 expression, and elevated infiltration by antitumor CTLs and NK cells attracted by elevated CXCL9 and CXCL10 levels. Importantly, the HIF inhibitor boosted the percentage of mice with a complete response to anti-PD-1 CPI from 25% to 67% (23).
Also in this issue of the JCI, Bailey, Liu, et al. demonstrate that pharmaceutical or genetic targeting of HIF-1α abrogated PD-L1-mediated immune evasion by suppressing PD-L1 expression on malignant cells and myeloid regulatory cells (MDSCs and TAMs), causing reactivation of TILs and tumor rejection (24). HIF-1α inhibition in vivo downregulated PD-1 expression, upregulated cytolytic effector molecules granzyme B and perforin, and decreased apoptosis in TILs in the tumor microenvironment. Importantly, HIF-1α inhibition potentiated immunotherapeutic effects of anti-CTLA-4 therapy in experimental models. The authors also reported an interesting observation; although HIF-1α inhibition blocked PD-L1 expression at the tumor site, it induced PD-L1 in normal tissues. This finding is important since high incidences of immune-related adverse events associated with CPI therapy are well documented. Thus, it is conceivable that HIF-1α inhibition represents a promising companion for


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Isobaric Tags for Relative and Absolute Quantitation Identification of Blood Proteins Relevant to Paroxetine Response in Patients With Major Depressive Disorder

Objectives Isobaric tags for relative and absolute quantitation (iTRAQ) is a proteomic investigation that could be utilized for rapid identification and quantification of proteins, which we would use to identify differentially expressed proteins in treatment responsive patients with major depressive disorder (MDD). Methods Six treatment responsive patients of MDD were recruited, and their peripheral blood mononuclear cell (PBMC) were collected before and after 4 weeks of paroxetine treatment. iTRAQ and Mascot search engine were used to detect differentially expressed proteins, which were then validated by Western blot. Results Two thousand one hundred and fifty three proteins were screened, and seven proteins showed differences of more than two-fold and 62 proteins with a differences of less than two-fold. Six proteins with commercially available antibodies were identified, and were validated by Western blot in 10 paroxetine responsive MDD patients. Putative hydroxypyruvate isomerase (HYI), eukaryotic translation initiation factor 4H (eIF4H), and RNA binding motif 8A (RBM8A) had statistically significant differences before and after treatment in the validation. Data are available via ProteomeXchange with identifier PXD028947. Conclusions By using iTRAQ and Western blot, we were able to identify HYI, eIF4H, and RAM8a to be the potential predictors of paroxetine treatment response in patients with MDD. This finding could help establish future individualized medicine.

INTRODUCTION
Major depression disorder (MDD) is a mental illness characterized by pathological low mood, loss of interest or pleasure, and pessimistic thoughts that negatively impact the quality of life of a significant proportion of the world's population, and has a lifetime incidence ranging from 4.4 to 20% (1). In several decades, many studies have tried to identify the underlying pathogenic mechanisms for MDD (2). However, the pathogenesis of the MDD and related biological regulatory mechanisms are still largely unknown. Therefore, utilization of new technologies to search for potential biomarkers in MDD is mandated.
After the successful deconstruction of the human genome, it was first thought that decoding the gene would be sufficient to unravel the mystery of human brain. However, it was found that genes alone do not necessarily represent the phenotypes, thus entered the post-genome exploration, where the human proteomics program aims at the systematic study of protein expressions.
Earlier proteomic studies frequently utilized cerebrospinal fluid (CSF) or postmortem brain tissues to identify the disease relevant molecules in MDD. The known pathogenesis of MDD involves many brain regions, but most studies focus on one regulatory pathway at a time (3,4). In clinical population, CSF is challenging to collect, let alone brain tissues. Therefore, investigations of peripheral blood of patients of MDD allowed easier access and flexibility, such as comparing the proteomic changes before and after antidepressant treatment (5). Peripheral blood mononuclear cell (PBMC) has been suggested to be an ideal peripheral meidrum in searching of biomarker (5). Functional lymphatic vessels found in dural sinuses (6) suggested a possible crosstalk between central nervous system (CNS) and the peripheral (7). PBMC has been utilized in both animal (5) and clinical studies of MDD (8,9).
In the past, we analyzed the peripheral blood of patients with MDD, using acid hydrolysis and modern mass spectrometry, and found that the ratio of transferrin to fibrinogen was significantly different between patients and healthy controls (10,11). The same technique has been successfully applied to serum samples of patients with schizophrenia, before and after antipsychotic treatment. In that study we found that the serum IgG content changed significantly after risperidone, a frequently prescribed antipsychotic, treatment (12). Furthermore, we also observed that disulfide-isomerase A3 and F-actin-capping protein subunit beta in platelet samples of patients with MDD have higher performance than those of healthy controls using two-dimensional differential gel electrophoresis (2D-DIGE) (13).
2D-DIGE, however, is limited by the range of isoelectric point and molecular weight, and the development of isobaric tags for relative and absolute quantitation (iTRAQ) could be utilized for rapid identification and quantification of proteins. Wang et al. used the iTRAQ method to study relevant proteins in human serum and quickly screened 472 proteins, of which 154 were found to be differentially expressed between patients with depression and healthy conrtols (14). In 2015, Han et al. used iTRAQ technology to study hippocampal postsynaptic densityassociated proteins in animal models, screened 1,500 proteins, and found that 74 membrane proteins were differentially expressed (15). Palmfeldt et al. investigated the prefrontal cortices of rat models of depression with iTRAQ and found several pathways that may contribute to the development of new therapeutic directions (16).
Another clinical challenge for managing MDD is that at least a third of patients experienced inadequate response from antidepressant treatment (17). Pharmacogenetics could help identify the most suitable antidepressant based on an individual's genetic profile on pharmacokinetics and pharmacodynamics (18), though individual genotyping remains expensive. Commercial iTRAQ for discovery of target proteins is also expensive, but if the discovered peripheral target proteins are validated, the cost of measuring only identified peripheral markers becomes more affordable.
In this study, iTRAQ was used to analyze PBMC in patients with MDD, before and after 4 weeks of antidepressant paroxetine, a selective serotonin reuptake inhibitor (SSRI), treatment, in order to identify proteins relevant to paroxetine treatment response.

Study Samples
The patients were recruited from a tertiary medical center over an 1-year period. Their age was between 20 and 65 years old, and they had no systemic diseases such as diabetes, hypertension, and hyperthyroidism. They also had no smoking or alcohol addiction. All participants had the ability to sign the informed consent, after the investigators thoroughly explained the details of the study. The diagnosis of MDD was established by the same senior psychiatrist, according to the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria. Patients were free of antidepressants or other medications for at least 1 month prior to entering the study. The severity of MDD was assessed with 17-item Hamilton Depression Rating Scale (HAM-D) (19). All patients had scores greater or equal to 18 prior to treatment. All patients received 4 weeks of paroxetine treatment. Treatment response was defined by HAM-D score reduction of >50%. PBMC of patients were collected in the morning, after fasting for 6 h, before and after 4 weeks of antidepressant treatment. Only those showing treatment response were analyzed (20)(21)(22). Written informed consent was provided by all participants after the content and context of the study was fully explained. The institutional review board (IRB) of Chang Gung Memorial Hospital approved the study design (IRB number 201601596B0).

Isobaric Tags for Relative and Absolute Quantification Labeling
Equal amounts of samples at baseline were pooled, and equal amounts of the samples after treatment were pooled, to minimize the effect of individual variation. One hundred micrograms of protein was reduced, blocked on cysteine, alkylated, and subsequently digested with trypsin overnight at 37 • C. The resulting products were than labeled with isobaric tags using a 4-plex iTRAQ kit. Isobaric tags 114 and 115 were used to label lithium responsive groups before and after treatment, respectively, and isobaric tags 116 and 117 were used to label paroxetine responsive groups before and after treatment, respectively. A similar design decision in which only reagents 114 and 116 were used had been seen before to cut down experimental cost (23).

Peptide Fractionation
The peptides labeled with different channels of isobaric tags were pooled and desalted by using Sep-Pak C18 cartridges (Waters). The desalted peptides were dried under vacuum and then resuspended in 0.5% trifluoroacetic acid for further fractionized by reversed phase C18 under high pH condition (Pierce High pH Reversed-Phase Peptide Fractionation Kit, Thermo Fisher Scientific, USA) as manufacturer's suggested protocol. The resulting bound peptides were eluted in 1% ammonia solution with various amounts of acetonitrile (5-50 %) resulting in about 10 fractions. Each eluted fraction was then dried under vacuum and re-suspended in 0.1% formic acid solution for the liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis.

Tandem Mass Spectrometry-Based Protein Identification
Tandem mass spectrometry analysis was performed on Q Exactive TM HF mass spectrometer (Thermo Fisher, San Jose, California, USA) in combination with a Thermo Scientific TM UltiMate TM 3000 RSLCnano HPLC system. The peptide mixtures were directly loaded onto a 50-cm analytic column (EASY-Spray TM C18 Column), and separated by a gradient with gradually increased of buffer B (80% acetonitrile in 0.1% formic acid) at a flow rate of 250 nl/min over about 165 min. The peptide spectra were acquired in the positive ion mode with a data-dependent acquisition. The top abundant 15 precursor ions within 375-1,400 m/z scan range were then dynamically selected for further fragmented in high collision dissociation (HCD) mode with normalized collision energy set to 33 ± 1. In Full MS scan, the resolution was set to 60,000 at m/z 200, AGC target to 3e6, maximum inject time to 50 ms. In MS/MS scan, the resolution was set to 15,000, with AGC target to 5e4, and maximum inject time set to 100 ms. The release of dynamic exclusion of the selected precursor ions was set to 20 s.

Mass Spectrometry-Based Proteomics Data Analysis by Proteome Discoverer Software and Mascot Search Engine
The MS files were uploaded into Proteome Discoverer (version 2.1, Thermo Scientific) to generate peak list for protein identification using Mascot search algorithm (version 2.5, Matrix Science) against Swiss-Prot human protein database. For protein identification, the following parameteres were used for database search: carbamidomethylation at Cys as the fixed modification, oxidation at Met, acetylation at protein Nterminus, iTRAQ-labeled at peptide N-terminus and K residue as dynamic modifications, maximum missing cleavage sites with 2, 10 ppm for MS tolerance, and 0.02 Da for MS/MS tolerance. The acceptable false discovery rate of peptide and protein identifications was set to <1%. A minimum of two unique peptides and quantification with two spectrum ratio counts from the unique peptides were required to improve the confidence of the protein identification. The median value of the spectrum ratios was calculated as protein abundance. The global median normalization was applied to recalculate the protein abundance to generate the normalized protein ratios to reduce the system error from sample preparation in each experiment. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (24) partner repository with the dataset identifier PXD028947 and 10.6019/PXD028947.

Western Blotting Validation
The volume of the loading buffer was one-fourth of the total volume. The proteins were denatured at 95 • C for 10 min. Following SDS-PAGE, the proteins were transferred to PVDF membranes. After blocking in 2% BSA in PBST for 1 h at room temperature, the PVDF membrane was incubated for 10 h at 4 • C with the following primary antibodies: antihydroxypyruvate isomerase (HYI) antibody (Abcam, Cambridge, UK), anti-alpha cardiac muscle 1 actin (ACTC1) antibody (R&D Systems, Minneapolis, MN, USA), anti-eukaryotic translation initiation factor 4H (eIF4H) antibody (Thermo Fisher Scientific, Waltham, MA, USA), anti-adenylate kinase isoenzyme 1 (AK1) antibody (Thermo Fisher Scientific, Waltham, MA, USA), anti-mitochondrial dynamin-like 120 kDa protein (OPA1) antibody (R&D Systems, Minneapolis, MN, USA), and anti-RNA binding motif 8A (RBM8A) antibody (Thermo Fisher Scientific, Waltham, MA, USA). Anti-cytochrome c oxidase (COX IV) antibody was used as controls (25). After three washes with PBST, the membrane was incubated with anti-goat or antirabbit secondary antibody (1:10,000) at room temperature for 1 h, and then the membrane was washed another five times with PBST (5 min each). The Western blot bands were photographed with UVP BioSpectrum 810 Imaging System (Thermo Fisher Scientific, Waltham, MA, USA). The relative intensities of the protein bands were calculated with ImageJ software (National Institutes of Health), using background subtraction method (26).

Protein-Protein Interactions
Protein-protein interactions were analyzed using BioGRID database (27). The interested protein names were inputed in the main page (https://thebiogrid.org/), searching with "By Protein/Gene" and "Homo sapiens."

Statistical Analysis of iTRAQ Data
The statistical method utilizing peptide-level intensities of iTRAQ reporter ions was used to determine which proteins are significantly changed (28).

Study Participants
We recruited six patients with MDD showing response after 4 weeks of paroxetine treatment. Their demographic data were summarized in Table 1. Samples from six patients were analyzed using iTRAQ-based quantitative proteomics.

iTRAQ-Based Quantitative Proteomics
Protein and peptide identifications with false determinate rate (FDR) < 0.01 and with at least one unique peptide screened 2,153 proteins. Seven proteins showed more than two-fold  difference before and after treatment, and 62 proteins showed differences less than two-fold ( Table 2). Six proteins with significant fold changes and commercially available antibodies were identified for further Western blot validation, and they are: putative hydroxypyruvate isomerase (HYI, fold change −311.80), alpha cardiac muscle 1 actin (ACTC1, fold change −14.27), eukaryotic translation initiation factor 4H (eIF4H, fold change −6.42), adenylate kinase isoenzyme 1 (AK1, fold change −3.64), mitochondrial dynamin-like 120 kDa protein (OPA1, fold change −3.55), and RNA binding motif 8A (RBM8A, fold change 2.000). Their representative MS/MS spectra annotated with b and y ions identified are shown in Supplementary Figures 1-6.

Western Blot Validation
Ten pairs of PBMC samples from 10 MDD patients with paroxetine response were analyzed with Western blot analysis (Figure 1). The blot intensities were quantified using ImageJ (Supplementary Table 2). The relative intensities were then calculated with background subtraction method (26) by dividing the intensity of target band by the control (COX-IV) intensity. The relative intensities of HYI, ACTC1, eIF4H, AK1, OPA1, and RBM8A before and after 4 weeks of paroxetine treatment are summarized in Supplementary Table 3. Using paired t-test, HYI, eIF4H, and RBM8A showed statistically significant difference before and after paroxetine treatment (p = 0.005, 0.019, and 0.013, respectively). Using Wilcoxon signed-rank test, HYI, eIF4H, and RBM8A showed statistically significant difference before and after paroxetine treatment (p = 0.013, 0.022, and 0.022, respectively) ( showing that eIF4H and RBM8a can be linked via eukaryotic translation initiation factor family. Those html pages were converted to PDF using a free online converter (https:// www.sejda.com/html-to-pdf), and the interested interactors were marked using PDF-XChange Viewer (Version 2.5: Build 322.10). Those html and PDF files were uploaded to a public data depository.

Peptide-Level Intensities of iTRAQ Reporters
A two-fold cut off was initially used, but after the western blots data we were suggested to evaluate the candidates using a statistical method utilizing peptide-level intensities of iTRAQ reporters to determine which proteins are significantly changed (28). This method could not be applied to HYI, eIF4H, and RBM8A due to their low numbers of peptide spectrum matches (and they all have only one unique peptide), but ACTC1, AK1, and OPA1 all had p values lower than 0.05 (p = 1.33 x 10 −55 , 0.00162, and 0.0125, respectively). p-values for proteins with two-fold increases or decreases were also added to Supplementary Table 1.

DISCUSSION
The major finding of this study was that by using iTRAQ, we were able to identify three proteins: putative hydroxypyruvate isomerase (HYI), eukaryotic translation initiation factor 4H (eIF4H), and RNA binding motif 8A (RBM8A), which were validated by Western blot, as the potential biomarkers for paroxetine treatment response in MDD patients.
It is the first time HYI and eIF4H were found to associate with psychiatric disorders. HYI catalyzes the reversible isomerization between hydroxypyruvate and 2-hydroxy-3-oxopropanoate (tartronate semialdehyde). HYI had not previously been FIGURE 1 | Western blots of 10 MDD patients with paroxetine reponse, before and after antidepressant treatment. A, after treatment; ACTC1, alpha cardiac muscle 1 actin; AK1, adenylate kinase isoenzyme 1; B, before treatment; COX-IV, cytochrome c oxidase subunit IV, as control; EIF4H, eukaryotic translation initiation factor 4H; HYI, putative hydroxypyruvate isomerase; MDD, major depressive disorder; OPA1, mitochondrial dynamin-like 120 kDa protein; RBM8A, RNA binding motif 8A. associated with human diseases. eIF4H, in contrast, had been associated with various diseases, most prominently cancer. In a proteomic investigation of attached (surviving) and detached (dying) cancer cells, eIF4H was identified among six consistently differentially expressed proteins, and silencing it had an anti-proliferative effect (29). In lung adenocarcinoma, eIF4H was the target of miRNA519d to inhibit cell proliferation and invasion (30). eIF4H is overexpressed in lung cancer cells and can be used to predict the response of chemotherapy (31). eIF4h has been found to be associated with neurodegenerative diseases as well. In the post-mortem tissues of patients with amyotrophic lateral sclerosis and frontotemporal degeneration, eIF4H was downregulated in patients with G4C2 mutation compared to patients without the particular mutation and healthy controls, suggesting eIF4H as a disease modifier (32). In a study investigating spatial memory performance and associated transcriptomic alternations in hippocampus of mice underwent chronically unpredictable stress, eIF4H was among identified genes (33). On one hand, while there had been no direct association of HYI and eIF4H with MDD, our findings suggest further investigations would be warranted.
On the other hand, RBM8a had been associated with brain development and animal model of depression. RBM8a is one of the four core proteins of exon junction complex (EJC), along with Magoh, eIF4A3 (DDX48), and CASC3 (MLN51) (34). Microdeletions of 1q21.1 chromosome, which include Rbm8a gene, were associated with abnormal brain developments such as microcephaly or macrocephaly (35) as well as moderate mental retardation (36). In an animal study, over-expression of RBM8a in the dentate gyrus of the hippocampus of mice induced abnormal behaviors, in anxiety and depression models such as open field test, social interaction test, and forced swimming test (37). In terms of protein-protein interaction analysis using BioGRID database (27), RBM8a interacts with eIF4A3, which is linked with eIF4A1, which is further connected with eIF4H, suggesting the involvement of eukaryotic translation initiation factor family. Our data suggest RBM8a and eIF4H could be involved in the paroxetine treatment response in MDD patients.
Three other proteins identified through iTRAQ [alpha cardiac muscle 1 actin (ACTC1), adenylate kinase isoenzyme 1 (AK1), and mitochondrial dynamin-like 120 kDa protein (OPA1)] were not verified by the Western blot validations, but they could be linked to MDD in interesting ways.
Mutations of alpha cardiac muscle 1 actin (ACTC1) had been associated with hypertrophic cardiomyopathy (HCM) (38). Studies had shown that patients with HCM were more depressed (39,40). Furthermore, ACTC1 upregulation could be induced by over-expression of dysbindin (41), a protein found to be reduced in hippocampus in patients with schizophrenia (42,43). Those data suggested ACTC1 could be affected in psychiatric disorders.
Adenylate kinase (AK) plays a crucial role in energetic metabolism (44). Its isoenzyme AK1 had been found to associate with neurodegenerative diseases. Upregulation of AK1 in the frontal cortex was associated with stages 5-6 of Parkinson's disease (45). Immunization of recombinant AK1 in mice could induce Th1-response as an immune protection (46). MDD had been associated with an activation of the immune/inflammatory system, including changes in serum acute phase protein (47)(48)(49)(50) and cytokine levels (51)(52)(53)(54)(55). Our previous data also showed that cytokine levels differed between antidepressant responders and non-responders (56). The role of AK1 in MDD treatment response should be followed up in the future.
Mitochondrial dynamin-like 120 kDa protein (OPA1) is a GTPase that regulates mitochondrial fusion and cristae structure in the inner mitochondrial membrane (57). Mutation of OPA1 gene has long been associated with dominant optic atrophy (58), but neurodegenerative symptoms had also been reported, as such parkinsonism and dementia (59), multiple sclerosis-like disorder (60), and fatal infantile mitochondrial encephalomyopathy (61). Disturbances in mitochondrial structure and function could impair neuroplasticity (62), while we did not find any report directly linking OPA1 to MDD at the moment, but a follow-up with more clinical samples could be interesting.
Proteomics had been used in identifying novel biomarkers for MDD, mostly comparing the proteomic profiles of patients with MDD with healthy controls (63). Proteomic studies focusing on antidepressant response were far more scarce, let alone ones focusing on one particular drug (64). Utilization of iTRAQ was also relatively novel. In animal model of depression, iTRAQ analysis identified 241 differentially expressed proteins in hippocampi after Chinese herbal medicine xiaochaihutang (XCHT) treatment, and protein-protein interaction network analysis predicted cell division cycle and apoptosis regulator protein 1 (Ccar1) and Calretinin (Calb2) might play the central roles in XCHT's antidepressant network (65). Our investigation could be the first to use iTRAQ to identify differentially expressed proteins in paroxetine response in patients with MDD.
There were several limitations in our study. The study sample size was small. Samples from six paroxetine responsive patients with MDD were analyzed in the iTRAQ analysis, and only 10 were analyzed in the Western blot validation. Ideally, an independent large cohort should be used to validate the results obtained from iTRAQ analysis, and further analyses such as receiver operating characteristic (ROC) analysis and principal component analysis (PCA) should be performed. Secondly, while significant changes were shown in both iTRAQ and Western blot validation, the directions of changes were not always consistent, which may indicate the possibility of false positive results. Thirdly, only one iTRAQ experiment was performed to compare data before and after paroxetine treatment without experimental, biological, or technical replicate. At least one biological replcate was highly recommended in iTRAQ experiments to minimize variations (66), though by combining our samples we hoped to reduce random biological variations. Fourthly, p-values calculation should be performed prior to candidate selection, and proteins identified and quantified by a single peptide are often discarded from further analysis. The readers are warned against over-interpret our study results because of those limitations, and a larger sample size with more controlled variables will be needed before a firm conclusion could be made.
iTRAQ could rapidly identify and quantify differentially expressed proteins, despite being costly. In patients with MDD showing response to paroxetine treatment, putative hydroxypyruvate isomerase (HYI), eukaryotic translation initiation factor 4H (eIF4H), and RNA binding motif 8A (RBM8A) were differentially expressed before and after treatment, as identified by iTRAQ and validated by Western blot. Those identified proteins could be potential predictors of paroxetine treatment response in patients with MDD. This finding could help establish future individualized medicine.

DATA AVAILABILITY STATEMENT
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (24) partner repository with the dataset identifier PXD028947 and 10.6019/PXD028947.

ETHICS STATEMENT
The studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS
T-LH and JS contributed substantially to conception and design. T-LH and HS contributed to acqusition of data. HS, C-CL, and T-LH helped with analysis and interpretation of data. C-CL drafted the article and revised it critically for important intellectual content. T-LH approved the final draft. All authors contributed to the article and approved the submitted version.